Overview

Dataset statistics

Number of variables8
Number of observations11917
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory744.9 KiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

per_area_buildings is highly overall correlated with per_area_greenery and 2 other fieldsHigh correlation
per_area_greenery is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_residential_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_rural_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
publictransport_frequency has 3152 (26.4%) zerosZeros
per_area_greenery has 127 (1.1%) zerosZeros
per_area_water has 1286 (10.8%) zerosZeros
per_residential_road has 511 (4.3%) zerosZeros
per_rural_road has 4046 (34.0%) zerosZeros
per_highway has 10051 (84.3%) zerosZeros
per_active has 5161 (43.3%) zerosZeros

Reproduction

Analysis started2024-07-05 13:00:33.908096
Analysis finished2024-07-05 13:00:45.817626
Duration11.91 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

publictransport_frequency
Real number (ℝ)

ZEROS 

Distinct3591
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1359.7394
Minimum0
Maximum88188
Zeros3152
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:46.086611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median463
Q31514
95-th percentile5566.2
Maximum88188
Range88188
Interquartile range (IQR)1514

Descriptive statistics

Standard deviation2877.411
Coefficient of variation (CV)2.1161488
Kurtosis146.13582
Mean1359.7394
Median Absolute Deviation (MAD)463
Skewness8.369932
Sum16204015
Variance8279494
MonotonicityNot monotonic
2024-07-05T15:00:46.302809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3152
 
26.4%
48 32
 
0.3%
182 28
 
0.2%
128 27
 
0.2%
144 25
 
0.2%
156 22
 
0.2%
20 21
 
0.2%
60 20
 
0.2%
40 20
 
0.2%
80 19
 
0.2%
Other values (3581) 8551
71.8%
ValueCountFrequency (%)
0 3152
26.4%
1 2
 
< 0.1%
2 8
 
0.1%
4 2
 
< 0.1%
5 6
 
0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
10 10
 
0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
88188 1
< 0.1%
75939 1
< 0.1%
48042 1
< 0.1%
45014 1
< 0.1%
37283 1
< 0.1%
37041 1
< 0.1%
35760 1
< 0.1%
35339 1
< 0.1%
34718 1
< 0.1%
34256 1
< 0.1%

per_area_greenery
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11791
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.805503
Minimum0
Maximum37.044511
Zeros127
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:46.518946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0404691
Q14.9175434
median10.36415
Q323.005576
95-th percentile32.333458
Maximum37.044511
Range37.044511
Interquartile range (IQR)18.088032

Descriptive statistics

Standard deviation10.513673
Coefficient of variation (CV)0.76155672
Kurtosis-1.0868897
Mean13.805503
Median Absolute Deviation (MAD)7.1161062
Skewness0.538464
Sum164520.17
Variance110.53733
MonotonicityNot monotonic
2024-07-05T15:00:46.767607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 127
 
1.1%
8.124364084 1
 
< 0.1%
0.6242119655 1
 
< 0.1%
0.1014118074 1
 
< 0.1%
0.05175535624 1
 
< 0.1%
0.1937298859 1
 
< 0.1%
4.577803982 1
 
< 0.1%
11.90549964 1
 
< 0.1%
0.3448971126 1
 
< 0.1%
2.82524744 1
 
< 0.1%
Other values (11781) 11781
98.9%
ValueCountFrequency (%)
0 127
1.1%
3.317032207 × 10-51
 
< 0.1%
0.0002723586594 1
 
< 0.1%
0.000486533707 1
 
< 0.1%
0.001162488542 1
 
< 0.1%
0.001353928408 1
 
< 0.1%
0.001474888498 1
 
< 0.1%
0.001575386188 1
 
< 0.1%
0.00274766501 1
 
< 0.1%
0.004953091291 1
 
< 0.1%
ValueCountFrequency (%)
37.0445106 1
< 0.1%
36.45912927 1
< 0.1%
36.34074938 1
< 0.1%
36.32722163 1
< 0.1%
36.30877405 1
< 0.1%
36.27721351 1
< 0.1%
36.26314978 1
< 0.1%
36.12138475 1
< 0.1%
36.10050028 1
< 0.1%
36.08573839 1
< 0.1%

per_area_water
Real number (ℝ)

ZEROS 

Distinct10632
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2658032
Minimum0
Maximum27.855332
Zeros1286
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:47.057303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13611565
median0.68422047
Q31.756826
95-th percentile4.2893181
Maximum27.855332
Range27.855332
Interquartile range (IQR)1.6207104

Descriptive statistics

Standard deviation1.7660202
Coefficient of variation (CV)1.3951776
Kurtosis23.405704
Mean1.2658032
Median Absolute Deviation (MAD)0.64609443
Skewness3.6532541
Sum15084.576
Variance3.1188273
MonotonicityNot monotonic
2024-07-05T15:00:47.289064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1286
 
10.8%
1.798141851 1
 
< 0.1%
0.1148671049 1
 
< 0.1%
1.6473332 1
 
< 0.1%
0.07221213635 1
 
< 0.1%
0.3758598996 1
 
< 0.1%
3.126431768 1
 
< 0.1%
1.646729253 1
 
< 0.1%
0.4973626016 1
 
< 0.1%
1.089080529 1
 
< 0.1%
Other values (10622) 10622
89.1%
ValueCountFrequency (%)
0 1286
10.8%
2.651921877 × 10-91
 
< 0.1%
4.32031784 × 10-61
 
< 0.1%
3.326226009 × 10-51
 
< 0.1%
4.433164423 × 10-51
 
< 0.1%
5.116345485 × 10-51
 
< 0.1%
6.999181302 × 10-51
 
< 0.1%
0.0001076380094 1
 
< 0.1%
0.0001555025912 1
 
< 0.1%
0.0002347983376 1
 
< 0.1%
ValueCountFrequency (%)
27.8553322 1
< 0.1%
20.68321705 1
< 0.1%
20.18943923 1
< 0.1%
19.89993047 1
< 0.1%
19.24829082 1
< 0.1%
19.19503696 1
< 0.1%
19.07084704 1
< 0.1%
18.80182671 1
< 0.1%
17.40978865 1
< 0.1%
17.21246461 1
< 0.1%

per_area_buildings
Real number (ℝ)

HIGH CORRELATION 

Distinct11916
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5698887
Minimum0
Maximum23.944326
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:47.717708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21171857
Q11.0700872
median4.4288071
Q36.907551
95-th percentile11.093452
Maximum23.944326
Range23.944326
Interquartile range (IQR)5.8374639

Descriptive statistics

Standard deviation3.6329943
Coefficient of variation (CV)0.79498529
Kurtosis0.50393952
Mean4.5698887
Median Absolute Deviation (MAD)2.9125273
Skewness0.76798569
Sum54459.363
Variance13.198647
MonotonicityNot monotonic
2024-07-05T15:00:47.959653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
< 0.1%
5.869767731 1
 
< 0.1%
10.05132808 1
 
< 0.1%
0.3264963607 1
 
< 0.1%
1.318352548 1
 
< 0.1%
0.2525683574 1
 
< 0.1%
0.4218052547 1
 
< 0.1%
13.82367913 1
 
< 0.1%
19.41282828 1
 
< 0.1%
13.42002654 1
 
< 0.1%
Other values (11906) 11906
99.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.001292944406 1
< 0.1%
0.003463808998 1
< 0.1%
0.004593450554 1
< 0.1%
0.00533211301 1
< 0.1%
0.0064025628 1
< 0.1%
0.006660210787 1
< 0.1%
0.007195262851 1
< 0.1%
0.008709836381 1
< 0.1%
0.009328015353 1
< 0.1%
ValueCountFrequency (%)
23.94432635 1
< 0.1%
20.90929425 1
< 0.1%
20.73902374 1
< 0.1%
20.71522916 1
< 0.1%
20.5208185 1
< 0.1%
20.44453932 1
< 0.1%
20.27873112 1
< 0.1%
20.17996998 1
< 0.1%
19.90009826 1
< 0.1%
19.86178755 1
< 0.1%

per_residential_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9910
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.072923
Minimum0
Maximum100
Zeros511
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:48.238247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13361374
Q133.848526
median82.858059
Q396.544049
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)62.695523

Descriptive statistics

Standard deviation36.141436
Coefficient of variation (CV)0.54699314
Kurtosis-0.97879567
Mean66.072923
Median Absolute Deviation (MAD)16.870253
Skewness-0.78818164
Sum787391.03
Variance1306.2034
MonotonicityNot monotonic
2024-07-05T15:00:48.476480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1498
 
12.6%
0 511
 
4.3%
78.75597808 1
 
< 0.1%
94.42605452 1
 
< 0.1%
94.00734941 1
 
< 0.1%
95.58731005 1
 
< 0.1%
91.81424082 1
 
< 0.1%
99.55386719 1
 
< 0.1%
98.86641665 1
 
< 0.1%
99.99547302 1
 
< 0.1%
Other values (9900) 9900
83.1%
ValueCountFrequency (%)
0 511
4.3%
4.58240098 × 10-71
 
< 0.1%
4.343942585 × 10-51
 
< 0.1%
4.543501388 × 10-51
 
< 0.1%
0.0003486495193 1
 
< 0.1%
0.0004561188908 1
 
< 0.1%
0.0006493482008 1
 
< 0.1%
0.001110702669 1
 
< 0.1%
0.001485022487 1
 
< 0.1%
0.002078347923 1
 
< 0.1%
ValueCountFrequency (%)
100 1498
12.6%
99.99999952 1
 
< 0.1%
99.99999823 1
 
< 0.1%
99.99996975 1
 
< 0.1%
99.99994959 1
 
< 0.1%
99.99988674 1
 
< 0.1%
99.99972133 1
 
< 0.1%
99.9994591 1
 
< 0.1%
99.99942133 1
 
< 0.1%
99.99913776 1
 
< 0.1%

per_rural_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7578
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.012347
Minimum0
Maximum100
Zeros4046
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:48.700815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.7335915
Q352.221197
95-th percentile97.375426
Maximum100
Range100
Interquartile range (IQR)52.221197

Descriptive statistics

Standard deviation34.617574
Coefficient of variation (CV)1.2815463
Kurtosis-0.5629714
Mean27.012347
Median Absolute Deviation (MAD)7.7335915
Skewness1.0021782
Sum321906.14
Variance1198.3764
MonotonicityNot monotonic
2024-07-05T15:00:48.907919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4046
34.0%
100 295
 
2.5%
5.030007044 1
 
< 0.1%
99.60157021 1
 
< 0.1%
85.44253131 1
 
< 0.1%
90.90969474 1
 
< 0.1%
60.51075118 1
 
< 0.1%
22.69396976 1
 
< 0.1%
26.16951935 1
 
< 0.1%
27.38984636 1
 
< 0.1%
Other values (7568) 7568
63.5%
ValueCountFrequency (%)
0 4046
34.0%
4.769359204 × 10-71
 
< 0.1%
1.774655509 × 10-61
 
< 0.1%
3.462658419 × 10-61
 
< 0.1%
4.784888569 × 10-61
 
< 0.1%
3.024612199 × 10-51
 
< 0.1%
3.91713762 × 10-51
 
< 0.1%
0.0001906117797 1
 
< 0.1%
0.0005786731282 1
 
< 0.1%
0.00151619519 1
 
< 0.1%
ValueCountFrequency (%)
100 295
2.5%
99.99996478 1
 
< 0.1%
99.99965135 1
 
< 0.1%
99.99954388 1
 
< 0.1%
99.9988893 1
 
< 0.1%
99.99851498 1
 
< 0.1%
99.99792165 1
 
< 0.1%
99.99743194 1
 
< 0.1%
99.99686917 1
 
< 0.1%
99.99677108 1
 
< 0.1%

per_highway
Real number (ℝ)

ZEROS 

Distinct1867
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6314983
Minimum0
Maximum82.256893
Zeros10051
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:49.124080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.815686
Maximum82.256893
Range82.256893
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.060734
Coefficient of variation (CV)3.0631728
Kurtosis16.326835
Mean2.6314983
Median Absolute Deviation (MAD)0
Skewness3.808739
Sum31359.565
Variance64.975433
MonotonicityNot monotonic
2024-07-05T15:00:49.373933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10051
84.3%
3.554627364 1
 
< 0.1%
14.16525893 1
 
< 0.1%
54.84065782 1
 
< 0.1%
43.50097626 1
 
< 0.1%
13.77453992 1
 
< 0.1%
1.540471818 1
 
< 0.1%
1.797074737 1
 
< 0.1%
0.7790380045 1
 
< 0.1%
8.983421759 1
 
< 0.1%
Other values (1857) 1857
 
15.6%
ValueCountFrequency (%)
0 10051
84.3%
3.522361211 × 10-51
 
< 0.1%
0.0007869091601 1
 
< 0.1%
0.000906819416 1
 
< 0.1%
0.001205115281 1
 
< 0.1%
0.003163704315 1
 
< 0.1%
0.01030289926 1
 
< 0.1%
0.01421078661 1
 
< 0.1%
0.01525235306 1
 
< 0.1%
0.01556955831 1
 
< 0.1%
ValueCountFrequency (%)
82.25689278 1
< 0.1%
73.31424592 1
< 0.1%
71.52999348 1
< 0.1%
69.37805266 1
< 0.1%
67.02430031 1
< 0.1%
66.95819755 1
< 0.1%
65.40683177 1
< 0.1%
64.55761274 1
< 0.1%
63.72402663 1
< 0.1%
63.62350891 1
< 0.1%

per_active
Real number (ℝ)

ZEROS 

Distinct6757
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.249666
Minimum0
Maximum95.804121
Zeros5161
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size93.2 KiB
2024-07-05T15:00:49.650789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.53814554
Q35.1630876
95-th percentile19.975654
Maximum95.804121
Range95.804121
Interquartile range (IQR)5.1630876

Descriptive statistics

Standard deviation7.9600363
Coefficient of variation (CV)1.8730969
Kurtosis17.060055
Mean4.249666
Median Absolute Deviation (MAD)0.53814554
Skewness3.4362522
Sum50643.27
Variance63.362178
MonotonicityNot monotonic
2024-07-05T15:00:49.884738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5161
43.3%
16.21401488 1
 
< 0.1%
4.436375986 1
 
< 0.1%
1.027933617 1
 
< 0.1%
1.252722925 1
 
< 0.1%
3.323474358 1
 
< 0.1%
3.203456228 1
 
< 0.1%
14.12770971 1
 
< 0.1%
9.976272422 1
 
< 0.1%
2.587428404 1
 
< 0.1%
Other values (6747) 6747
56.6%
ValueCountFrequency (%)
0 5161
43.3%
3.117331218 × 10-61
 
< 0.1%
1.013443002 × 10-51
 
< 0.1%
1.124275953 × 10-51
 
< 0.1%
1.276030506 × 10-51
 
< 0.1%
0.0001132630846 1
 
< 0.1%
0.000117898717 1
 
< 0.1%
0.000278665462 1
 
< 0.1%
0.0002949521794 1
 
< 0.1%
0.0004796564621 1
 
< 0.1%
ValueCountFrequency (%)
95.8041211 1
< 0.1%
86.73869382 1
< 0.1%
78.9129573 1
< 0.1%
75.15183741 1
< 0.1%
74.66697903 1
< 0.1%
74.04507086 1
< 0.1%
73.99864354 1
< 0.1%
72.90591117 1
< 0.1%
72.77557605 1
< 0.1%
72.43716519 1
< 0.1%

Interactions

2024-07-05T15:00:44.168453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:34.506339image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:36.175172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:37.577907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:38.942525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:40.299242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:41.634136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:42.903728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:44.322152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:34.734043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:36.434191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:37.717463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:39.096187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:40.467738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:41.816689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:43.050827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:44.491405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:34.962014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:36.650842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:37.878651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:39.258956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:40.634126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:41.963821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:43.220074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:44.650576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:35.172588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:36.807487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:38.041032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:39.412565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:40.784137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:42.101811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:43.367242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:44.848952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:35.419608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:36.954651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:38.341837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:39.584353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:40.946404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:42.248980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:43.557422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:44.985998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:35.600804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:37.092646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:38.464833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:39.734146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:41.084400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:42.449504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:43.717522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:45.124046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:35.774198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:37.255068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:38.617296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:39.898440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:41.315969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:42.600777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:43.871859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:45.257222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:35.936938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:37.408748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:38.767272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:40.116869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:41.463071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:42.750994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:00:44.021484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T15:00:50.065968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
per_activeper_area_buildingsper_area_greeneryper_area_waterper_highwayper_residential_roadper_rural_roadpublictransport_frequency
per_active1.0000.059-0.0950.1400.033-0.133-0.1650.151
per_area_buildings0.0591.000-0.890-0.052-0.2570.776-0.7930.221
per_area_greenery-0.095-0.8901.000-0.0480.240-0.7560.789-0.202
per_area_water0.140-0.052-0.0481.0000.061-0.011-0.0560.107
per_highway0.033-0.2570.2400.0611.000-0.3160.1750.038
per_residential_road-0.1330.776-0.756-0.011-0.3161.000-0.8840.188
per_rural_road-0.165-0.7930.789-0.0560.175-0.8841.000-0.211
publictransport_frequency0.1510.221-0.2020.1070.0380.188-0.2111.000

Missing values

2024-07-05T15:00:45.440097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T15:00:45.667401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
04271.08.1243641.7981425.86976878.7559785.0300070.00000016.214015
14031.015.0306460.9411003.25491381.03892713.7068570.0000005.254216
24664.012.0828911.2138493.42707676.7770589.5598489.0041794.658915
30.030.2894910.7365280.62235826.22771245.6093950.00000028.162893
4904.027.4975930.6166170.3527974.49566688.1363345.2910582.076942
50.032.7456950.5833320.24759111.47844068.5081978.92679711.086565
65551.09.6182301.5527335.27307797.5361682.2422540.0000000.221577
71246.032.6416680.4143850.31554945.59846449.2832942.0178013.100442
8829.016.2786051.6114272.76832965.11891834.8810820.0000000.000000
90.031.2195870.6850050.26839624.21231841.46026126.6499047.677517
publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
119070.026.6727280.0634800.85562518.47872078.0550210.0000003.466259
119081946.04.3226860.0291338.48070095.2604944.7395060.0000000.000000
119091945.02.4991750.1568857.43151594.6025465.3974540.0000000.000000
11910839.07.5263370.0000005.87298591.3948848.6051160.0000000.000000
11911559.09.6382640.0148436.16384695.8172394.1827610.0000000.000000
119120.029.6220710.0226350.87000713.67911386.3208870.0000000.000000
119130.031.5098520.2221360.5403430.25615299.7438480.0000000.000000
119140.023.8367260.9953471.63211631.48785668.3372640.1559000.018980
119150.028.6701960.7994670.7838593.99581968.82626327.1779180.000000
11916279.029.2728490.2027380.6429927.46102592.5389750.0000000.000000